code: def test_max_drift_score_condition_fail(
      tweet_emotion_train_test_textdata_sampled,
  ):
      # Arrange
      train, test = tweet_emotion_train_test_textdata_sampled
      check = TextEmbeddingsDrift().add_condition_overall_drift_value_less_than(
          0.1
      )
  
      # Act
      result = check.run(train, test, with_display=False)
      condition_result, *_ = check.conditions_decision(result)
  
      # Assert
      assert_that(
          condition_result,
          equal_condition_result(
              is_pass=False,
              name="Drift value is less than 0.1",
              details="Found drift value of: 0.12, corresponding to a domain classifier AUC of: 0.56",
          ),
      )
  
file_path: deepchecks/tests/nlp/checks/train_test_validation/text_embeddings_drift_test.py
imports: from sys import platform
  import numba
  from hamcrest import assert_that, close_to
  from deepchecks.nlp.checks import TextEmbeddingsDrift
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 5 for calling a global function with example usage: test_max_drift_score_condition_fail(tweet_emotion_train_test_textdata_sampled) without return types